• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.361-368
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• 2022

This study seeks to determine the research trend of late adolescents by utilizing big data. Also, seek for research trends related to activity participation, treatment, and mediation to provide academic implications. For this process, gathered 1.000 academic papers and used TF-IDF analysis method, and the topic modeling based on co-occurrence word network analysis method LDA (Latent Dirichlet Allocation) to analyze. In conclusion this study conducted analysis of activity participation, treatment, and mediation of late adolescents by TF-IDF analysis method, co-occurrence word network analysis method, and topic modeling analysis based on LDA(Latent Dirichlet Allocation). The results were proposed through visualization, and carries significance as this study analyzed activity, treatment, mediation factors of late adolescents, and provides new analysis methods to figure out the basic materials of activity participation trends, treatment, and mediation of late adolescents.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.12-20
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• 2024

Sentiment analysis has become a pivotal component in understanding public opinion, market trends, and user experiences across various domains. The advent of GPT (Generative Pre-trained Transformer) models has revolutionized the landscape of natural language processing, introducing a new dimension to sentiment analysis. This comprehensive roadmap delves into the transformative impact of GPT models on sentiment analysis tasks, contrasting them with conventional methodologies. With an increasing need for nuanced and context-aware sentiment analysis, this study explores how GPT models, known for their ability to understand and generate human-like text, outperform traditional methods in capturing subtleties of sentiment expression. We scrutinize various case studies and benchmarks, highlighting GPT models' prowess in handling context, sarcasm, and idiomatic expressions. This roadmap not only underscores the superior performance of GPT models but also discusses challenges and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and AI enthusiasts. The in-depth analysis provided in this paper serves as a testament to the transformational potential of GPT models in the realm of sentiment analysis.

• The Journal of Economics, Marketing and Management
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• v.6 no.3
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• pp.10-13
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• 2018

Purpose - On the basis of discussing the network economy concept and the commentary of the marginal revenue decreasing of traditional economic theory, The concept of network economy has just been put forward in recent years. The reason why such a concept appears is that the information technology, marked by computer network, plays an increasingly important role in economic activities. Some people define network economy as an economic form based on network technology and human capital. this paper points out network economy existing the marginal revenue increasing and analyzes the reasons that influencing the marginal revenue increasing. Research design, data, methodology - The network economy has fundamentally changed the traditional economic laws. The economic basis of industrial society is the law of incremental marginal cost, which reflects the socialization of high cost in industrial society. Results - As the number of network members increases, the value of the network increases explosively, and the value increases attract more members to join, resulting in more returns. Conclusion - In conclusion, network economy has changed many aspects of traditional economy, resulting in decreasing marginal cost, decreasing transaction cost in and out of enterprise organizations, and making the effect of increasing scale compensation more prominent. This is of great significance to the information construction in China.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.

• Educational Technology International
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• v.20 no.2
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• pp.169-194
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• 2019

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.193-194
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• 2014

This paper provides the market opportunity analysis process and technology providing matching process based on the efficient quantitative analysis of a large amount of papers and network analysis. In the future, it is required to analyze and complement the quantitative analysis result by commercialization experts and to build emerging issue database.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.213-220
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• 2014

This study suggests new approach to identify core technologies through patent analysis. Specially, the approach applied data mining technique and multi-criteria decision making method to the co-classification information of registered patents. First, technological interrelationship matrices of intensity, relatedness, and cross-impact perspectives are constructed with support, lift and confidence values calculated by conducting an association rule mining on the co-classification information of patent data. Second, the analytic network process is applied to the constructed technological interrelationship matrices in order to produce the importance values of technologies from each perspective. Finally, data envelopment analysis is employed to the derived importance values in order to identify priorities of technologies, putting three perspectives together. It is expected that suggested approach could help technology planners to formulate strategy and policy for technological innovation.

• Asian Journal of Innovation and Policy
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• v.9 no.3
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• pp.339-359
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• 2020

The policy change in the Data 3 Act is one of the issues that should be noted at a time when non-face-to-face business strategies become important after COVID-19. The Data 3 Act was implemented in South Korea on August 5, 2020, calling 'Big Data 3 Act' and 'Data Economy 3 Act,' and so personal information that was not able to identify a particular individual could be utilized without the consent of the individual. With the implementation of the Data 3 Act, it is possible to establish a fair economic ecosystem by ensuring fair access to data and various uses. In this paper, the law on the protection of personal information, which is the core of the Data 3 Act, was compared around Korea, the European Union and the United States, and the implications were derived through network analysis of keywords.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.663-670
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• 1988

Advances in VLSI technology have made it possible to have a larger number of processing elements to be included in highly parallel processor system. A system with a large number of processing elements and memory requires a complex data path. Multistage Interconnection networks(MINS) are useful in providing programmable data path between processing elements and memory modules in multiprocessor system. In this thesis, the performance of MINS for the star network has been analyzed and compared with other networks, such as generalized shuffle network, delta network, and referenced crossbar network.